An adaptive psychometric procedure that places each trial at the current most probable Baye& ian estimate of threshold is described. The procedure takes advantage of the common finding that the human psychometric function is invariant in form when expressed as a function of log intensity

Summary: We consider estimating a probability density p based on a random sample from this density by a Bayesian approach. The prior is constructed in two steps, by first constructing priors on a collection of models each expressing a qualitative prior guess on the true density, and next combining

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of an extrasolar planet, and locating a hidden one-dimensional object—show the approach can significantly improve observational efficiency in settings that have well-defined nonlinear models. I conclude with a list of open issues that must be addressed to make Bayesian adaptive exploration a practical and reliable

We report the results of some preliminary experiments with a new method of acoustic-phonetic modeling for large vocabulary applications that can be viewed as a far-reaching extension of Bayesian speaker adaptation. This method adapts all of the Gaussian mean vectors in a speaker-independent HMM

. In addition, it severely lacks the desired probabilistic formulation. In this article, we propose a Bayesian adaptive nearest neighbor method (BANN) that can adaptively select the shape of the neighborhood and the number of neighbors k. The shape of the neighborhood is automatically selected according

to build systems on such data. Here, transforms are used to represent the different acoustic conditions, and then a canonical model is trained given this set of transforms. This paper describes a Bayesian framework for adaptive training and inference. This framework addresses some limitations of standard

but effective GMMs for likelihood functions, a universal background model (UBM) for alternative speaker representation, and a form of Bayesian adaptation to derive speaker models from the UBM. The development and use of a handset detector and score normalization to greatly improve verification performance

Sequence alignment without the specification of gap penalties or a scoring matrix is attained by using Bayesian inference and a recursive algorithm. This procedure’s recursive algorithm sums over all possible alignments on the forward step to obtain normalizing constants essential to Bayesian

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